Lights, camera, action!

Today, we’re going to take a deep dive into the world of HBO movies and TV shows. From the iconic dramas like The Sopranos and Game of Thrones to the latest releases. HBO has been providing quality content to its viewers for decades, but have you ever wondered how they make decisions about what shows to produce or which movies to acquire? That’s where the fascinating world of HBO data comes into play. By analyzing audience trends, ratings, and viewer demographics, HBO can make informed decisions about what to offer to its loyal fans. So sit back, grab a snack, and get ready to explore the exciting world of HBO data.

Installing packages

#install.packages("ggrepel")
#install.packages("ggiraph") ## to use geom_tooltip
#install.packages("ggiraphExtra")
#load tidyverse to manipulate data
#load ggplot2 for graphing
#load shiny to...
#load dplyer to manipulate data
#load knitr for general-purpose literate programming
#load kableExtra to add features to table

library(ggrepel) ## For using tooltip
library(ggiraph)  ## For using geom_tooltip
library(ggiraphExtra)
library(tidyverse)
library(ggplot2)
library(shiny)
library(dplyr)
library(countrycode)
library(knitr)
library(kableExtra)
library(maps)

About Our Data

The data we’ve decided to work on is from kaggle and is owned by Diego Enrique and here’s the link: https://www.kaggle.com/datasets/dgoenrique/hbo-max-movies-and-tv-shows

Titles data:

15 variables, 3030 observations

id: The title ID

title: The name of the title

show_type: Tv show or Movie

description: A description of movie or tv show

release_year: Year show/movie was released

age_certification: The age rating of movie or show

runtime: The length of the episode of show or movie in minutes

genres: A list of genres

production_countries: Countries that produced the show/movie

seasons: Number of seasons IF it is a show

imdb_id: The title ID on IMDB

imdb_score: Score on IMDB

imdb_votes: Votes on IMDB

tmdb_popularity: Popularity on TMDB

tmdb_score: Score on TMDB

Credits data:

5 variables, 64879 observations

person_ID: The person ID on JustWatch

id: The title ID on JustWatch

name: The name of actor or director

character_name: The name of character played in movie/show

role: ACTOR or DIRECTOR

Let us read our datas, shall we?

We’re using the kable and head function to show a part of the data sets we’re working on but in an organized manner

Here’s our credits.csv

And here’s our titles.csv

What if we try to combine these data sets?

both_data <- inner_join(titles, credits, by = "id")

kable(head(both_data),
      align = "c",
      caption = "<b><center>Sample table of both data",
      format = "html") %>% 
  kable_styling(bootstrap_options = "bordered", full_width = FALSE)

Let’s begin by determining the number or movies and TV shows we are working with

Wow! that’s a lot more movies than shows! But let’s see it visually

What’s the distribution of genres for both Shows and Movies in our dataset?

Here’s the table of number of genres in descending order

Since we just finished oberserving the number of genres in our dataset

Let us see if there’s a correlation between age_restriction and genres

## Unique age certifications:  PG, G, PG-13, R, TV-G, TV-Y, TV-Y7, TV-PG, NC-17, TV-14, TV-MA, TV-Y7-FV

Well that did not work as expected. Let’s see if a geom_tile graph does the job:

Now lets look at our Actor and Director columns in our credits data.

Since actors and directors can have multiple projects, lets remove the duplicates

Are any of these actors/directors in multiple projects? If so, who was in the most projects?

Here are the number of shows available in HBO by release year

Here’s the summary table of what the graph shows

This indicates us that HBO primarily features Movies and Shows from the decade of 2010s

You can see there is a wide range of movies and tv shows, especially what year they were released. I wonder what the oldest movies and shows are?

Now let’s see what are the top 10 most popular movies and show from imbd and tmdb

Here are HBO’s top 10 movies and shows according to imdb score

Here are HBO’s top 10 movies and shows according to tmdb score

Let’s see if we can get a correlation from both imdb and tmdb scores find an average

Now explore if there’s a relationship between longest movie and its popularity?

Ever wonder if your most favourite genre happens to be the most popular genre on HBO?

Let’s find out

Here’s genre vs popularity graph based on movies and shows

Who would’ve know?!

Last but not least, let us look at the Number of movies and TV shows by country

Unfortunately, because HBO only got their movies and shows from 99 countries, there are some uncolored countries

countries = titles %>%
  mutate(production_countries = str_remove_all(production_countries, "'")) %>% 
  mutate(production_countries = gsub("\\[", "", production_countries)) %>% 
  mutate(production_countries = gsub("\\]", "", production_countries)) %>% 
  separate_rows(production_countries, sep = ", ") %>%
  group_by(production_countries, type) %>%
  summarize(total = n()) %>%
  arrange(desc(total))

## Removing rows that has country unknown
countries = filter(countries, !is.na(production_countries) & production_countries != "") 

## Creating a new column that represents the full country name of production countries
countries$full_country_name <- countrycode(sourcevar = countries$production_countries, origin = "iso2c", destination= "country.name")




kable(countries,
      align = "c",
      caption = "<b><center>Number of movies and TV shows by country",
      format = "html")%>% 
    kable_styling(bootstrap_options = "bordered", full_width = FALSE)

# Renaming "United States" to "USA" to match with data in world_map
countries <- countries %>% 
  mutate(full_country_name = if_else(full_country_name == "United States", "USA", full_country_name))

## We're going to try to show this data using a world map
world_map = map_data("world")
  

## Let's join our world map and our countries data
world_map_data= full_join(world_map, countries, by = c("region" = "full_country_name"))

## Plotting
 
  ggplot(world_map_data, aes(x = long, y = lat, group = group, fill = total)) +
  geom_polygon() +
  coord_equal() +
  scale_fill_gradient2(limits = c(0, 1824), low = "lightblue", mid = "blue", high = "darkblue", midpoint = 900) +
  labs(title = "Number of movies and TV shows by country", fill = "Count") + 
  theme_bw()